Models and Theories in Human-Computer Interaction/Diffusion Innovation and TAM 2

Diffusion of Innovation in the WWW (Eric Andren)
Defining an audience, demographic, or group of clientele as potential adopters based on the Diffusion of Innovation theory provides great feedback towards understanding whether a solution accomplishes the goals defined by a given problem. When creating web interfaces the internet serves as an instant outlet for a product or solution to be viewed and taken advantage of by millions of users instantly. Being able to understand the perceived desire and ease of use of a web application serves as a great advantage when making decision during the design process. The ability for so many users to be able to have first hand use of an application of website provides a great outlet for expanding on friability and allows for mass observability making the interface more adoptable. First hand accounts provide direct feedback on perceived complexity of difficulty which will inform a solutions perceived compatibility within the current zeitgeist of web values and methods. If a solution can take full control of observability, trial ability, complexity, and compatibility it will then be able to make a strong case for its relative advantage by being a desirable and adoptable solution. This makes the Diffusion of Innovation theory a great tool when creating web/applications for a wide range of functionality and demographics. This can also help remove or lessen the period of the needs paradox hopefully helping or encouraging laggards and others adopt to technologies that are valuable in their daily activities.

Mind the Gap! Misses of the Diffusion of Innovation Theory(Liechty)
The Diffusion of Innovation Theory covers well the adoption of successful products, but misses the mark on products that never make it to mainstream acceptance. From my experience with Prototyping Electric Vehicles the most difficult part of innovation is not engineering a product, nor is it in finding a fellow innovator or an early adopter to utilize the innovation, but is instead the challenge of gaining the momentum necessary to reach the Early Majority. In the case of most unsuccessful products the Early Majority is never reached. There is a lot to be gained in filling in the information in this gap to better predict which products will succeed in their diffusion, and which will not. The likely reason this has not been done is that it would require a great deal more context and add more complexity to the theory. This would lead to a system that is not deterministic but probabilistic, and would bring in the less than scientific concept of luck. Overall, the Diffusion of Innovation Theory describes well the path of a successful innovation, but does not address the positions where an innovation can lose steam or become obsolete. Incorporating these characteristics would require a dynamic model and add complexity.

Diffusion Model adapted for a marketing study (Scott Keen)
The structure and vast adoption of the Diffusion model is the most valuable aspect of the model when needing to adapt for a new study. There is a wide range of possibilities in its adaption to several user experience research methods. When having to contend with a wide variety of stakeholders including board members this frameworks’ wide spread adoption and application makes it an easier sell to those making further decisions. This will add value and relevance to the study and strengthen the foundation as it is being adapted for the context of the study.

3 User Experience Research concepts that can be adapted into the Diffusion Model


 * 1) Focus on storytelling - The process uses information-exchange about innovation as a part of the discovery period would be beneficial in other user experience methods that explore the use of narrative and storytelling. Interviews with adopters and using their stories and even having a sample section keep diaries.
 * 2) Creating personas based on real data - The development of extensive personas would play off the user interviews and questions. Developing them based on real data would be ideal for representing to stakeholders schemas based less on metaphor.
 * 3) Empathy - Ability to dig deep into the behavioral analysis of the users and empathize with the user.
 * 1) Empathy - Ability to dig deep into the behavioral analysis of the users and empathize with the user.
 * 1) Empathy - Ability to dig deep into the behavioral analysis of the users and empathize with the user.

The Diffusion model should play well with User Experience methods.